Most approaches to learn classifiers for structured objects (e.g., images) use generative models in a classical Bayesian framework. However, state-of-the-art classifiers for vecto...
Abstract. In this paper we initiate an exploration of relationships between “preference elicitation”, a learning-style problem that arises in combinatorial auctions, and the pr...
Avrim Blum, Jeffrey C. Jackson, Tuomas Sandholm, M...
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
Abstract. Technology-enhanced learning has gained momentum in European Higher Education, especially in recent years. In what way this movement has influenced organisations, their ...
A number of natural models for learning in the limit is introduced to deal with the situation when a learner is required to provide a grammar covering the input even if only a par...